Overview

Brought to you by YData

Dataset statistics

Number of variables16
Number of observations52413
Missing cells117953
Missing cells (%)14.1%
Duplicate rows2068
Duplicate rows (%)3.9%
Total size in memory8.8 MiB
Average record size in memory176.3 B

Variable types

Numeric14
Categorical2

Alerts

14617_FERM0101.DO_2_PV has constant value "0.0"Constant
Dataset has 2068 (3.9%) duplicate rowsDuplicates
14617_FERM0101.PUMP_1_PV is highly imbalanced (> 99.9%)Imbalance
14617_FERM0101.Agitation_PV has 4446 (8.5%) missing valuesMissing
14617_FERM0101.Air_Sparge_PV has 4446 (8.5%) missing valuesMissing
14617_FERM0101.Biocontainer_Pressure_PV has 4446 (8.5%) missing valuesMissing
14617_FERM0101.DO_1_PV has 4446 (8.5%) missing valuesMissing
14617_FERM0101.DO_2_PV has 51263 (97.8%) missing valuesMissing
14617_FERM0101.Gas_Overlay_PV has 4446 (8.5%) missing valuesMissing
14617_FERM0101.Load_Cell_Net_PV has 4446 (8.5%) missing valuesMissing
14617_FERM0101.pH_1_PV has 4446 (8.5%) missing valuesMissing
14617_FERM0101.pH_2_PV has 4446 (8.5%) missing valuesMissing
14617_FERM0101.PUMP_1_PV has 4446 (8.5%) missing valuesMissing
14617_FERM0101.PUMP_1_TOTAL has 4446 (8.5%) missing valuesMissing
14617_FERM0101.PUMP_2_PV has 4446 (8.5%) missing valuesMissing
14617_FERM0101.PUMP_2_TOTAL has 4446 (8.5%) missing valuesMissing
14617_FERM0101.Single_Use_DO_PV has 4446 (8.5%) missing valuesMissing
14617_FERM0101.Single_Use_pH_PV has 4446 (8.5%) missing valuesMissing
14617_FERM0101.Temperatura_PV has 4446 (8.5%) missing valuesMissing
14617_FERM0101.Agitation_PV has 34452 (65.7%) zerosZeros
14617_FERM0101.Air_Sparge_PV has 45976 (87.7%) zerosZeros
14617_FERM0101.DO_1_PV has 32772 (62.5%) zerosZeros
14617_FERM0101.Gas_Overlay_PV has 31673 (60.4%) zerosZeros
14617_FERM0101.Load_Cell_Net_PV has 10498 (20.0%) zerosZeros
14617_FERM0101.PUMP_1_TOTAL has 4773 (9.1%) zerosZeros
14617_FERM0101.PUMP_2_PV has 44757 (85.4%) zerosZeros
14617_FERM0101.PUMP_2_TOTAL has 7917 (15.1%) zerosZeros

Reproduction

Analysis started2024-09-29 18:20:20.159473
Analysis finished2024-09-29 18:20:37.242020
Duration17.08 seconds
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

14617_FERM0101.Agitation_PV
Real number (ℝ)

MISSING  ZEROS 

Distinct394
Distinct (%)0.8%
Missing4446
Missing (%)8.5%
Infinite0
Infinite (%)0.0%
Mean13.849633
Minimum0
Maximum84
Zeros34452
Zeros (%)65.7%
Negative0
Negative (%)0.0%
Memory size2.8 MiB
2024-09-29T20:20:37.287952image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q320
95-th percentile80
Maximum84
Range84
Interquartile range (IQR)20

Descriptive statistics

Standard deviation26.725745
Coefficient of variation (CV)1.9297078
Kurtosis1.7957193
Mean13.849633
Median Absolute Deviation (MAD)0
Skewness1.8362083
Sum664325.34
Variance714.26545
MonotonicityNot monotonic
2024-09-29T20:20:37.360018image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 34452
65.7%
80 5817
 
11.1%
20 5569
 
10.6%
36 1595
 
3.0%
84 56
 
0.1%
76.59500122 39
 
0.1%
40 16
 
< 0.1%
44 13
 
< 0.1%
76.57999878 12
 
< 0.1%
76.60999756 8
 
< 0.1%
Other values (384) 390
 
0.7%
(Missing) 4446
 
8.5%
ValueCountFrequency (%)
0 34452
65.7%
20 5569
 
10.6%
20.10310695 1
 
< 0.1%
20.21899309 1
 
< 0.1%
20.67293457 1
 
< 0.1%
20.95280672 1
 
< 0.1%
20.98088829 1
 
< 0.1%
20.99491044 1
 
< 0.1%
21.05531766 1
 
< 0.1%
21.21556392 1
 
< 0.1%
ValueCountFrequency (%)
84 56
 
0.1%
80 5817
11.1%
79.93813568 1
 
< 0.1%
79.93371996 1
 
< 0.1%
79.8770863 1
 
< 0.1%
79.76942245 1
 
< 0.1%
79.70800067 1
 
< 0.1%
79.6834771 1
 
< 0.1%
79.67000122 1
 
< 0.1%
79.64278647 1
 
< 0.1%

14617_FERM0101.Air_Sparge_PV
Real number (ℝ)

MISSING  ZEROS 

Distinct1992
Distinct (%)4.2%
Missing4446
Missing (%)8.5%
Infinite0
Infinite (%)0.0%
Mean1.6758164
Minimum0
Maximum160.05262
Zeros45976
Zeros (%)87.7%
Negative0
Negative (%)0.0%
Memory size2.8 MiB
2024-09-29T20:20:37.429621image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum160.05262
Range160.05262
Interquartile range (IQR)0

Descriptive statistics

Standard deviation9.4171246
Coefficient of variation (CV)5.619425
Kurtosis35.829147
Mean1.6758164
Median Absolute Deviation (MAD)0
Skewness5.9764025
Sum80383.885
Variance88.682236
MonotonicityNot monotonic
2024-09-29T20:20:37.501742image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 45976
87.7%
14.06279144 1
 
< 0.1%
63.99588593 1
 
< 0.1%
63.99154193 1
 
< 0.1%
64.0060196 1
 
< 0.1%
63.99904114 1
 
< 0.1%
24.87028503 1
 
< 0.1%
39.44444275 1
 
< 0.1%
64.04457215 1
 
< 0.1%
58.19633405 1
 
< 0.1%
Other values (1982) 1982
 
3.8%
(Missing) 4446
 
8.5%
ValueCountFrequency (%)
0 45976
87.7%
0.521129714 1
 
< 0.1%
0.5400095941 1
 
< 0.1%
0.5457789322 1
 
< 0.1%
0.6422712586 1
 
< 0.1%
0.7333871287 1
 
< 0.1%
0.7423152386 1
 
< 0.1%
1.136883879 1
 
< 0.1%
1.15745287 1
 
< 0.1%
1.201952457 1
 
< 0.1%
ValueCountFrequency (%)
160.0526175 1
< 0.1%
120.9136629 1
< 0.1%
64.54693604 1
< 0.1%
64.52896118 1
< 0.1%
64.4909729 1
< 0.1%
64.44514523 1
< 0.1%
64.428302 1
< 0.1%
64.40546265 1
< 0.1%
64.40250598 1
< 0.1%
64.32200928 1
< 0.1%

14617_FERM0101.Biocontainer_Pressure_PV
Real number (ℝ)

MISSING 

Distinct12232
Distinct (%)25.5%
Missing4446
Missing (%)8.5%
Infinite0
Infinite (%)0.0%
Mean307.40952
Minimum-13.325246
Maximum480
Zeros0
Zeros (%)0.0%
Negative13247
Negative (%)25.3%
Memory size2.8 MiB
2024-09-29T20:20:37.577333image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum-13.325246
5-th percentile-1.9341936
Q1-0.14458844
median480
Q3480
95-th percentile480
Maximum480
Range493.32525
Interquartile range (IQR)480.14459

Descriptive statistics

Standard deviation230.86484
Coefficient of variation (CV)0.75100096
Kurtosis-1.6512982
Mean307.40952
Median Absolute Deviation (MAD)0
Skewness-0.59024174
Sum14745512
Variance53298.574
MonotonicityNot monotonic
2024-09-29T20:20:37.651945image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
480 30769
58.7%
-0.7841430664 177
 
0.3%
-0.1157409668 145
 
0.3%
-0.8044006348 143
 
0.3%
-0.3385437012 132
 
0.3%
-0.358795166 126
 
0.2%
-0.7638916016 124
 
0.2%
0.1273132324 120
 
0.2%
-0.1359924316 116
 
0.2%
0.08680419922 113
 
0.2%
Other values (12222) 16002
30.5%
(Missing) 4446
 
8.5%
ValueCountFrequency (%)
-13.32524637 1
< 0.1%
-13.28509749 1
< 0.1%
-13.27101081 1
< 0.1%
-13.26063193 1
< 0.1%
-13.26021567 1
< 0.1%
-13.25839985 1
< 0.1%
-13.25374049 1
< 0.1%
-13.25243813 1
< 0.1%
-13.24671344 1
< 0.1%
-13.24433251 1
< 0.1%
ValueCountFrequency (%)
480 30769
58.7%
18.87250625 1
 
< 0.1%
13.41627286 1
 
< 0.1%
13.08221435 1
 
< 0.1%
10.25646126 1
 
< 0.1%
10.24052199 1
 
< 0.1%
10.20762642 1
 
< 0.1%
10.10356234 1
 
< 0.1%
10.08214415 1
 
< 0.1%
9.777979469 1
 
< 0.1%

14617_FERM0101.DO_1_PV
Real number (ℝ)

MISSING  ZEROS 

Distinct3292
Distinct (%)6.9%
Missing4446
Missing (%)8.5%
Infinite0
Infinite (%)0.0%
Mean2.7917169
Minimum-0.28136597
Maximum99.436316
Zeros32772
Zeros (%)62.5%
Negative9263
Negative (%)17.7%
Memory size2.8 MiB
2024-09-29T20:20:37.726031image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum-0.28136597
5-th percentile-0.28136597
Q10
median0
Q30
95-th percentile22.203763
Maximum99.436316
Range99.717682
Interquartile range (IQR)0

Descriptive statistics

Standard deviation9.0688689
Coefficient of variation (CV)3.2484916
Kurtosis25.001762
Mean2.7917169
Median Absolute Deviation (MAD)0
Skewness4.4341461
Sum133910.28
Variance82.244384
MonotonicityNot monotonic
2024-09-29T20:20:37.797127image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 32772
62.5%
-0.2813659668 9254
 
17.7%
24.08387146 55
 
0.1%
23.9621933 42
 
0.1%
23.7231781 41
 
0.1%
23.48416443 35
 
0.1%
23.1234726 33
 
0.1%
23.60149841 27
 
0.1%
23.36248627 26
 
< 0.1%
24.20555115 24
 
< 0.1%
Other values (3282) 5658
 
10.8%
(Missing) 4446
 
8.5%
ValueCountFrequency (%)
-0.2813659668 9254
17.7%
-0.2218725676 1
 
< 0.1%
-0.1898950697 1
 
< 0.1%
-0.1892120976 1
 
< 0.1%
-0.1504498587 1
 
< 0.1%
-0.1471444051 1
 
< 0.1%
-0.1356840012 1
 
< 0.1%
-0.09656236762 1
 
< 0.1%
-0.09238882492 1
 
< 0.1%
-0.0571876545 1
 
< 0.1%
ValueCountFrequency (%)
99.43631592 1
< 0.1%
88.63425903 1
< 0.1%
84.27712402 1
< 0.1%
84.18154907 1
< 0.1%
81.64790039 1
< 0.1%
81.02649536 1
< 0.1%
80.88638916 1
< 0.1%
80.57045898 1
< 0.1%
80.25758057 1
< 0.1%
80.16330847 1
< 0.1%

14617_FERM0101.DO_2_PV
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)0.1%
Missing51263
Missing (%)97.8%
Memory size2.8 MiB
0.0
1150 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3450
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 1150
 
2.2%
(Missing) 51263
97.8%

Length

2024-09-29T20:20:37.862214image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-29T20:20:37.909294image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 1150
100.0%

Most occurring characters

ValueCountFrequency (%)
0 2300
66.7%
. 1150
33.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3450
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 2300
66.7%
. 1150
33.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3450
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 2300
66.7%
. 1150
33.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3450
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 2300
66.7%
. 1150
33.3%

14617_FERM0101.Gas_Overlay_PV
Real number (ℝ)

MISSING  ZEROS 

Distinct16295
Distinct (%)34.0%
Missing4446
Missing (%)8.5%
Infinite0
Infinite (%)0.0%
Mean1.4110866
Minimum0
Maximum20.157114
Zeros31673
Zeros (%)60.4%
Negative0
Negative (%)0.0%
Memory size2.8 MiB
2024-09-29T20:20:37.964365image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q33.9998521
95-th percentile4.0003124
Maximum20.157114
Range20.157114
Interquartile range (IQR)3.9998521

Descriptive statistics

Standard deviation2.1157218
Coefficient of variation (CV)1.4993565
Kurtosis6.565751
Mean1.4110866
Median Absolute Deviation (MAD)0
Skewness1.7455764
Sum67685.59
Variance4.4762787
MonotonicityNot monotonic
2024-09-29T20:20:38.041996image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 31673
60.4%
4.000101158 1
 
< 0.1%
3.998828754 1
 
< 0.1%
3.999933234 1
 
< 0.1%
3.997330053 1
 
< 0.1%
4.000116772 1
 
< 0.1%
3.999808339 1
 
< 0.1%
3.999752439 1
 
< 0.1%
4.000805914 1
 
< 0.1%
4.000103093 1
 
< 0.1%
Other values (16285) 16285
31.1%
(Missing) 4446
 
8.5%
ValueCountFrequency (%)
0 31673
60.4%
0.3994594589 1
 
< 0.1%
0.399586808 1
 
< 0.1%
0.3996747047 1
 
< 0.1%
0.3997357694 1
 
< 0.1%
0.3998233359 1
 
< 0.1%
0.3998823969 1
 
< 0.1%
0.3998975966 1
 
< 0.1%
0.3999046079 1
 
< 0.1%
0.3999054803 1
 
< 0.1%
ValueCountFrequency (%)
20.157114 1
< 0.1%
19.77040351 1
< 0.1%
16.63833811 1
< 0.1%
16.02903213 1
< 0.1%
16.00904304 1
< 0.1%
16.00787394 1
< 0.1%
16.00360584 1
< 0.1%
16.00334386 1
< 0.1%
16.00311356 1
< 0.1%
16.00308737 1
< 0.1%

14617_FERM0101.Load_Cell_Net_PV
Real number (ℝ)

MISSING  ZEROS 

Distinct1411
Distinct (%)2.9%
Missing4446
Missing (%)8.5%
Infinite0
Infinite (%)0.0%
Mean440.52991
Minimum-26.8
Maximum1690.8
Zeros10498
Zeros (%)20.0%
Negative18103
Negative (%)34.5%
Memory size2.8 MiB
2024-09-29T20:20:38.115571image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum-26.8
5-th percentile-19.6
Q1-1.2
median0
Q31552.4
95-th percentile1660
Maximum1690.8
Range1717.6
Interquartile range (IQR)1553.6

Descriptive statistics

Standard deviation714.41842
Coefficient of variation (CV)1.6217251
Kurtosis-0.98159457
Mean440.52991
Median Absolute Deviation (MAD)4.8
Skewness0.99892947
Sum21130898
Variance510393.67
MonotonicityNot monotonic
2024-09-29T20:20:38.190182image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 10498
20.0%
-1.2 7477
 
14.3%
0.4 2955
 
5.6%
-18 1755
 
3.3%
-20 1607
 
3.1%
-19.2 1146
 
2.2%
-0.4 1026
 
2.0%
-17.2 949
 
1.8%
-17.6 810
 
1.5%
1.2 777
 
1.5%
Other values (1401) 18967
36.2%
(Missing) 4446
 
8.5%
ValueCountFrequency (%)
-26.8 2
 
< 0.1%
-25.6 1
 
< 0.1%
-24 1
 
< 0.1%
-23.6 1
 
< 0.1%
-22.8 2
 
< 0.1%
-22.4 3
 
< 0.1%
-22 6
 
< 0.1%
-21.6 40
0.1%
-21.4706807 1
 
< 0.1%
-21.47027313 1
 
< 0.1%
ValueCountFrequency (%)
1690.8 1
 
< 0.1%
1686.4 1
 
< 0.1%
1686.257327 1
 
< 0.1%
1683.532882 1
 
< 0.1%
1682.4 1
 
< 0.1%
1676.4 5
 
< 0.1%
1676 16
< 0.1%
1675.920587 1
 
< 0.1%
1675.917904 1
 
< 0.1%
1675.6 16
< 0.1%

14617_FERM0101.pH_1_PV
Real number (ℝ)

MISSING 

Distinct4411
Distinct (%)9.2%
Missing4446
Missing (%)8.5%
Infinite0
Infinite (%)0.0%
Mean2.8089297
Minimum-0.098877716
Maximum7.3847786
Zeros1
Zeros (%)< 0.1%
Negative2
Negative (%)< 0.1%
Memory size2.8 MiB
2024-09-29T20:20:38.264253image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum-0.098877716
5-th percentile1.4264652
Q11.6986334
median1.7072765
Q34.0615554
95-th percentile5.883225
Maximum7.3847786
Range7.4836563
Interquartile range (IQR)2.3629221

Descriptive statistics

Standard deviation1.7933123
Coefficient of variation (CV)0.63843262
Kurtosis-0.91855498
Mean2.8089297
Median Absolute Deviation (MAD)0.12008629
Skewness0.87728471
Sum134735.93
Variance3.2159691
MonotonicityNot monotonic
2024-09-29T20:20:38.336842image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.707276535 13834
26.4%
1.587190247 3254
 
6.2%
1.426465225 2695
 
5.1%
3.701474762 2124
 
4.1%
1.699413681 2010
 
3.8%
1.754734039 1336
 
2.5%
1.617261124 994
 
1.9%
1.761300468 796
 
1.5%
1.387585449 692
 
1.3%
1.769512177 660
 
1.3%
Other values (4401) 19572
37.3%
(Missing) 4446
 
8.5%
ValueCountFrequency (%)
-0.09887771606 2
 
< 0.1%
0 1
 
< 0.1%
0.05516175767 1
 
< 0.1%
0.09475669861 465
 
0.9%
0.1857286453 504
 
1.0%
1.234505653 3
 
< 0.1%
1.358329773 576
 
1.1%
1.387585449 692
 
1.3%
1.40952301 102
 
0.2%
1.426465225 2695
5.1%
ValueCountFrequency (%)
7.384778595 1
< 0.1%
7.036100349 1
< 0.1%
6.925970143 1
< 0.1%
6.440647437 1
< 0.1%
6.381013253 1
< 0.1%
6.360365931 1
< 0.1%
6.31604864 1
< 0.1%
6.288689423 1
< 0.1%
6.270260368 1
< 0.1%
6.258161131 1
< 0.1%

14617_FERM0101.pH_2_PV
Real number (ℝ)

MISSING 

Distinct150
Distinct (%)0.3%
Missing4446
Missing (%)8.5%
Infinite0
Infinite (%)0.0%
Mean-0.1475101
Minimum-1.9690506
Maximum5.8804626
Zeros58
Zeros (%)0.1%
Negative20742
Negative (%)39.6%
Memory size2.8 MiB
2024-09-29T20:20:38.409417image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum-1.9690506
5-th percentile-1.9690506
Q1-1.9690506
median1.2423117
Q31.2423117
95-th percentile1.2423117
Maximum5.8804626
Range7.8495132
Interquartile range (IQR)3.2113623

Descriptive statistics

Standard deviation1.5771683
Coefficient of variation (CV)-10.691934
Kurtosis-1.8806511
Mean-0.1475101
Median Absolute Deviation (MAD)0
Skewness-0.25576757
Sum-7075.6169
Variance2.4874598
MonotonicityNot monotonic
2024-09-29T20:20:38.484518image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.242311668 26562
50.7%
-1.969050598 19862
37.9%
-1.474404144 807
 
1.5%
0.1882167816 485
 
0.9%
0 58
 
0.1%
-0.08967628479 45
 
0.1%
4.602012253 3
 
< 0.1%
4.209164047 2
 
< 0.1%
4.268426895 2
 
< 0.1%
0.4138712699 1
 
< 0.1%
Other values (140) 140
 
0.3%
(Missing) 4446
 
8.5%
ValueCountFrequency (%)
-1.969050598 19862
37.9%
-1.968660417 1
 
< 0.1%
-1.954904932 1
 
< 0.1%
-1.953966074 1
 
< 0.1%
-1.942430316 1
 
< 0.1%
-1.941695419 1
 
< 0.1%
-1.916553858 1
 
< 0.1%
-1.915684722 1
 
< 0.1%
-1.79533771 1
 
< 0.1%
-1.474404144 807
 
1.5%
ValueCountFrequency (%)
5.880462646 1
 
< 0.1%
5.632914734 1
 
< 0.1%
4.915260569 1
 
< 0.1%
4.614262791 1
 
< 0.1%
4.605470657 1
 
< 0.1%
4.602012253 3
< 0.1%
4.598425674 1
 
< 0.1%
4.591380692 1
 
< 0.1%
4.559614182 1
 
< 0.1%
4.277024841 1
 
< 0.1%

14617_FERM0101.PUMP_1_PV
Categorical

IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing4446
Missing (%)8.5%
Memory size2.8 MiB
0.0
47966 
38.21760524876984
 
1

Length

Max length17
Median length3
Mean length3.0002919
Min length3

Characters and Unicode

Total characters143915
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 47966
91.5%
38.21760524876984 1
 
< 0.1%
(Missing) 4446
 
8.5%

Length

2024-09-29T20:20:38.557121image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-29T20:20:38.608191image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 47966
> 99.9%
38.21760524876984 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 95933
66.7%
. 47967
33.3%
8 3
 
< 0.1%
2 2
 
< 0.1%
7 2
 
< 0.1%
6 2
 
< 0.1%
4 2
 
< 0.1%
3 1
 
< 0.1%
1 1
 
< 0.1%
5 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 143915
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 95933
66.7%
. 47967
33.3%
8 3
 
< 0.1%
2 2
 
< 0.1%
7 2
 
< 0.1%
6 2
 
< 0.1%
4 2
 
< 0.1%
3 1
 
< 0.1%
1 1
 
< 0.1%
5 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 143915
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 95933
66.7%
. 47967
33.3%
8 3
 
< 0.1%
2 2
 
< 0.1%
7 2
 
< 0.1%
6 2
 
< 0.1%
4 2
 
< 0.1%
3 1
 
< 0.1%
1 1
 
< 0.1%
5 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 143915
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 95933
66.7%
. 47967
33.3%
8 3
 
< 0.1%
2 2
 
< 0.1%
7 2
 
< 0.1%
6 2
 
< 0.1%
4 2
 
< 0.1%
3 1
 
< 0.1%
1 1
 
< 0.1%
5 1
 
< 0.1%

14617_FERM0101.PUMP_1_TOTAL
Real number (ℝ)

MISSING  ZEROS 

Distinct137
Distinct (%)0.3%
Missing4446
Missing (%)8.5%
Infinite0
Infinite (%)0.0%
Mean64.224683
Minimum0
Maximum287.68032
Zeros4773
Zeros (%)9.1%
Negative0
Negative (%)0.0%
Memory size2.8 MiB
2024-09-29T20:20:38.664649image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q124.800002
median49.599991
Q369.439984
95-th percentile287.68032
Maximum287.68032
Range287.68032
Interquartile range (IQR)44.639983

Descriptive statistics

Standard deviation66.363623
Coefficient of variation (CV)1.033304
Kurtosis4.6549292
Mean64.224683
Median Absolute Deviation (MAD)22.319989
Skewness2.1878177
Sum3080665.4
Variance4404.1304
MonotonicityNot monotonic
2024-09-29T20:20:38.736740image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
49.59999084 14392
27.5%
86.79997559 4774
 
9.1%
0 4773
 
9.1%
69.43998413 4545
 
8.7%
287.6803223 2500
 
4.8%
12.4 1771
 
3.4%
9.919999695 1626
 
3.1%
213.2801758 1425
 
2.7%
66.95998535 1397
 
2.7%
99.19996948 1141
 
2.2%
Other values (127) 9623
18.4%
(Missing) 4446
 
8.5%
ValueCountFrequency (%)
0 4773
9.1%
0.07288134248 1
 
< 0.1%
0.09831839343 1
 
< 0.1%
0.1558522886 1
 
< 0.1%
0.3928211376 1
 
< 0.1%
0.6834257263 1
 
< 0.1%
0.8258776739 1
 
< 0.1%
0.8796590074 1
 
< 0.1%
0.9597338459 1
 
< 0.1%
1.279374619 1
 
< 0.1%
ValueCountFrequency (%)
287.6803223 2500
4.8%
229.5887535 1
 
< 0.1%
225.0604981 1
 
< 0.1%
224.3762123 1
 
< 0.1%
213.2801758 1425
2.7%
205.8401611 2
 
< 0.1%
193.4401489 6
 
< 0.1%
181.0401245 4
 
< 0.1%
168.6401001 50
 
0.1%
161.2000854 164
 
0.3%

14617_FERM0101.PUMP_2_PV
Real number (ℝ)

MISSING  ZEROS 

Distinct2876
Distinct (%)6.0%
Missing4446
Missing (%)8.5%
Infinite0
Infinite (%)0.0%
Mean0.25268473
Minimum0
Maximum48
Zeros44757
Zeros (%)85.4%
Negative0
Negative (%)0.0%
Memory size2.8 MiB
2024-09-29T20:20:38.810101image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1.1483371
Maximum48
Range48
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.2018841
Coefficient of variation (CV)4.7564573
Kurtosis77.349268
Mean0.25268473
Median Absolute Deviation (MAD)0
Skewness6.2766134
Sum12120.528
Variance1.4445254
MonotonicityNot monotonic
2024-09-29T20:20:38.881271image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 44757
85.4%
8 282
 
0.5%
7.610464478 21
 
< 0.1%
4.800292969 4
 
< 0.1%
0.1925262451 4
 
< 0.1%
5.3284052 4
 
< 0.1%
0.361391449 3
 
< 0.1%
2.400146484 3
 
< 0.1%
0.4289131165 2
 
< 0.1%
1.025171661 2
 
< 0.1%
Other values (2866) 2885
 
5.5%
(Missing) 4446
 
8.5%
ValueCountFrequency (%)
0 44757
85.4%
0.0008672121488 1
 
< 0.1%
0.002711765529 1
 
< 0.1%
0.003089800865 1
 
< 0.1%
0.005893559857 1
 
< 0.1%
0.006319927927 1
 
< 0.1%
0.006464476075 1
 
< 0.1%
0.008863561709 1
 
< 0.1%
0.009584875345 1
 
< 0.1%
0.009614412571 1
 
< 0.1%
ValueCountFrequency (%)
48 1
 
< 0.1%
8 282
0.5%
7.999808115 1
 
< 0.1%
7.998277201 1
 
< 0.1%
7.997517177 1
 
< 0.1%
7.99626271 1
 
< 0.1%
7.9961447 1
 
< 0.1%
7.995279122 1
 
< 0.1%
7.99445011 1
 
< 0.1%
7.993969818 1
 
< 0.1%

14617_FERM0101.PUMP_2_TOTAL
Real number (ℝ)

MISSING  ZEROS 

Distinct4536
Distinct (%)9.5%
Missing4446
Missing (%)8.5%
Infinite0
Infinite (%)0.0%
Mean3686.0754
Minimum0
Maximum9733.4953
Zeros7917
Zeros (%)15.1%
Negative0
Negative (%)0.0%
Memory size2.8 MiB
2024-09-29T20:20:38.950929image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11653.925
median2014.7377
Q36974.4906
95-th percentile8224.4727
Maximum9733.4953
Range9733.4953
Interquartile range (IQR)5320.5656

Descriptive statistics

Standard deviation3072.6087
Coefficient of variation (CV)0.83357186
Kurtosis-1.4446821
Mean3686.0754
Median Absolute Deviation (MAD)2014.7377
Skewness0.3689617
Sum1.7680998 × 108
Variance9440924.2
MonotonicityNot monotonic
2024-09-29T20:20:39.025082image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2014.737695 14386
27.4%
0 7917
15.1%
7684.094531 4559
 
8.7%
6431.4 2980
 
5.7%
7353.300781 2498
 
4.8%
8224.472656 2215
 
4.2%
6974.490625 1421
 
2.7%
6436.503516 1420
 
2.7%
2068.205859 1281
 
2.4%
9733.495312 747
 
1.4%
Other values (4526) 8543
16.3%
(Missing) 4446
 
8.5%
ValueCountFrequency (%)
0 7917
15.1%
0.3685405601 1
 
< 0.1%
0.5414046329 1
 
< 0.1%
0.712801218 77
 
0.1%
1.539999962 1
 
< 0.1%
1.664988326 1
 
< 0.1%
3.527600479 1
 
< 0.1%
3.740000534 1
 
< 0.1%
5.860414563 1
 
< 0.1%
7.178785056 1
 
< 0.1%
ValueCountFrequency (%)
9733.495312 747
1.4%
9726.574219 1
 
< 0.1%
9710.925 1
 
< 0.1%
9677.501562 1
 
< 0.1%
9621.399219 1
 
< 0.1%
9568.690625 1
 
< 0.1%
9516.520313 1
 
< 0.1%
9462.626562 1
 
< 0.1%
9408.087126 1
 
< 0.1%
9398.410156 14
 
< 0.1%

14617_FERM0101.Single_Use_DO_PV
Real number (ℝ)

MISSING 

Distinct5912
Distinct (%)12.3%
Missing4446
Missing (%)8.5%
Infinite0
Infinite (%)0.0%
Mean665.56874
Minimum0
Maximum799.99199
Zeros36
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size2.8 MiB
2024-09-29T20:20:39.095188image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile18.886434
Q1731.65542
median799.99199
Q3799.99199
95-th percentile799.99199
Maximum799.99199
Range799.99199
Interquartile range (IQR)68.336572

Descriptive statistics

Standard deviation254.87746
Coefficient of variation (CV)0.38294686
Kurtosis2.2605398
Mean665.56874
Median Absolute Deviation (MAD)0
Skewness-1.989021
Sum31925336
Variance64962.519
MonotonicityNot monotonic
2024-09-29T20:20:39.171808image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
799.9919922 26044
49.7%
734.0049805 4572
 
8.7%
741.7635742 2869
 
5.5%
562.4240234 2723
 
5.2%
731.6554199 2314
 
4.4%
661.752002 765
 
1.5%
668.7777832 686
 
1.3%
738.8861328 672
 
1.3%
647.4522461 405
 
0.8%
730.252832 396
 
0.8%
Other values (5902) 6521
 
12.4%
(Missing) 4446
 
8.5%
ValueCountFrequency (%)
0 36
0.1%
0.2735763601 1
 
< 0.1%
0.382732047 1
 
< 0.1%
1.326502482 1
 
< 0.1%
1.439416181 1
 
< 0.1%
1.441547314 1
 
< 0.1%
1.457331102 1
 
< 0.1%
1.4895731 1
 
< 0.1%
1.510649341 1
 
< 0.1%
1.54550833 1
 
< 0.1%
ValueCountFrequency (%)
799.9919922 26044
49.7%
793.296952 1
 
< 0.1%
786.5282593 1
 
< 0.1%
760.8863618 1
 
< 0.1%
753.6987261 1
 
< 0.1%
753.448291 8
 
< 0.1%
751.4863456 1
 
< 0.1%
751.0956543 183
 
0.3%
747.691063 1
 
< 0.1%
746.6082783 1
 
< 0.1%

14617_FERM0101.Single_Use_pH_PV
Real number (ℝ)

MISSING 

Distinct542
Distinct (%)1.1%
Missing4446
Missing (%)8.5%
Infinite0
Infinite (%)0.0%
Mean492.03269
Minimum-794.36797
Maximum800.18398
Zeros0
Zeros (%)0.0%
Negative1471
Negative (%)2.8%
Memory size2.8 MiB
2024-09-29T20:20:39.244411image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum-794.36797
5-th percentile5.6240234
Q15.8959961
median799.87197
Q3800.03999
95-th percentile800.07998
Maximum800.18398
Range1594.552
Interquartile range (IQR)794.14399

Descriptive statistics

Standard deviation434.23703
Coefficient of variation (CV)0.88253695
Kurtosis0.006106933
Mean492.03269
Median Absolute Deviation (MAD)0.16801758
Skewness-1.0182299
Sum23601332
Variance188561.8
MonotonicityNot monotonic
2024-09-29T20:20:39.318072image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
800.0399902 14960
28.5%
5.895996094 4633
 
8.8%
5.624023438 3243
 
6.2%
800.0160156 2991
 
5.7%
799.8160156 2974
 
5.7%
800.0799805 2760
 
5.3%
799.8319824 1727
 
3.3%
799.9120117 1605
 
3.1%
799.8640137 1388
 
2.6%
5.727978516 1340
 
2.6%
Other values (532) 10346
19.7%
(Missing) 4446
 
8.5%
ValueCountFrequency (%)
-794.3679688 121
 
0.2%
-794.1120117 274
 
0.5%
-794.0959961 1
 
< 0.1%
-788.6240234 1
 
< 0.1%
-788.4799805 1
 
< 0.1%
-788.4560059 1
 
< 0.1%
-788.4399902 688
1.3%
-788.4080078 1
 
< 0.1%
-788.4 12
 
< 0.1%
-788.3983423 1
 
< 0.1%
ValueCountFrequency (%)
800.1839844 1128
 
2.2%
800.1199707 174
 
0.3%
800.0799805 2760
 
5.3%
800.0399902 14960
28.5%
800.0160156 2991
 
5.7%
799.9120117 1605
 
3.1%
799.8719727 1135
 
2.2%
799.8640137 1388
 
2.6%
799.8319824 1727
 
3.3%
799.8160156 2974
 
5.7%

14617_FERM0101.Temperatura_PV
Real number (ℝ)

MISSING 

Distinct26641
Distinct (%)55.5%
Missing4446
Missing (%)8.5%
Infinite0
Infinite (%)0.0%
Mean16.674519
Minimum3.0320007
Maximum37.616003
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 MiB
2024-09-29T20:20:39.390976image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum3.0320007
5-th percentile4.7878371
Q114.494048
median15.939192
Q317.951895
95-th percentile29.607996
Maximum37.616003
Range34.584003
Interquartile range (IQR)3.4578471

Descriptive statistics

Standard deviation6.4424892
Coefficient of variation (CV)0.38636733
Kurtosis0.25423617
Mean16.674519
Median Absolute Deviation (MAD)1.8048025
Skewness0.39495887
Sum799826.63
Variance41.505667
MonotonicityNot monotonic
2024-09-29T20:20:39.461418image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
29.60799561 955
 
1.8%
29.59200439 837
 
1.6%
29.63199463 725
 
1.4%
29.56800537 510
 
1.0%
29.64000244 260
 
0.5%
23.2 228
 
0.4%
29.55999756 205
 
0.4%
23.21600342 175
 
0.3%
23.19200439 142
 
0.3%
3.191998291 111
 
0.2%
Other values (26631) 43819
83.6%
(Missing) 4446
 
8.5%
ValueCountFrequency (%)
3.032000732 1
 
< 0.1%
3.047998047 1
 
< 0.1%
3.064001465 1
 
< 0.1%
3.069302299 1
 
< 0.1%
3.07199707 4
< 0.1%
3.080161001 1
 
< 0.1%
3.088321318 1
 
< 0.1%
3.096002197 9
< 0.1%
3.106560762 1
 
< 0.1%
3.106573962 1
 
< 0.1%
ValueCountFrequency (%)
37.61600342 1
 
< 0.1%
31.6503416 1
 
< 0.1%
31.50400391 1
 
< 0.1%
31.39428046 1
 
< 0.1%
31.31999512 1
 
< 0.1%
31.25136376 1
 
< 0.1%
31.24799805 5
 
< 0.1%
31.24687081 1
 
< 0.1%
31.24678064 1
 
< 0.1%
31.24000244 24
< 0.1%

Interactions

2024-09-29T20:20:35.749660image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:20.766807image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:21.742675image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:22.741971image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:23.719499image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:24.652631image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:27.949632image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:28.930538image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:29.895617image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:30.868090image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:31.835329image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:32.775108image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:33.744024image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:34.744780image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:35.814157image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:20.851876image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:21.808868image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:22.813289image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:23.780648image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:24.723001image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:28.015676image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:28.993929image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:29.960251image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
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2024-09-29T20:20:31.142673image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:32.097072image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:33.042159image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:34.023518image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
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2024-09-29T20:20:21.135638image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:22.092316image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
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2024-09-29T20:20:24.046838image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
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2024-09-29T20:20:32.235631image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
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2024-09-29T20:20:21.271960image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:22.237054image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
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2024-09-29T20:20:24.182636image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:25.153920image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:28.437209image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:29.415534image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:30.379282image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:31.354358image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:32.303746image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:33.265081image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:34.244532image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
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2024-09-29T20:20:36.303375image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:21.342145image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:22.309839image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
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2024-09-29T20:20:30.449043image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:31.425543image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:32.371364image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:33.336775image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:34.317709image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:35.322433image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:36.372484image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:21.408999image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:22.380522image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:23.375953image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:24.317888image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:27.589128image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:28.578619image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:29.553403image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:30.517664image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:31.491945image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:32.438374image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:33.403994image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
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2024-09-29T20:20:35.394768image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:36.436638image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:21.471108image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
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2024-09-29T20:20:23.442564image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:24.381439image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:27.657740image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:28.646992image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:29.618505image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:30.584369image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:31.557623image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:32.501613image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:33.469275image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:34.455817image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:35.462346image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:36.504587image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:21.536334image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
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2024-09-29T20:20:30.651568image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:31.623777image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:32.565727image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
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2024-09-29T20:20:34.597090image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
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2024-09-29T20:20:21.676917image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:22.671141image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:23.650506image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:24.586637image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:27.878755image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:28.860563image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:29.829252image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:30.798930image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:31.767167image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:32.708065image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:33.678316image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:34.674452image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:35.679802image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Missing values

2024-09-29T20:20:36.727446image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
A simple visualization of nullity by column.
2024-09-29T20:20:36.877076image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-09-29T20:20:37.079542image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

14617_FERM0101.Agitation_PV14617_FERM0101.Air_Sparge_PV14617_FERM0101.Biocontainer_Pressure_PV14617_FERM0101.DO_1_PV14617_FERM0101.DO_2_PV14617_FERM0101.Gas_Overlay_PV14617_FERM0101.Load_Cell_Net_PV14617_FERM0101.pH_1_PV14617_FERM0101.pH_2_PV14617_FERM0101.PUMP_1_PV14617_FERM0101.PUMP_1_TOTAL14617_FERM0101.PUMP_2_PV14617_FERM0101.PUMP_2_TOTAL14617_FERM0101.Single_Use_DO_PV14617_FERM0101.Single_Use_pH_PV14617_FERM0101.Temperatura_PV
DateTime
2023-03-15 00:00:00.0000.00.0480.00.0NaN0.00.01.707277-1.9690510.049.5999910.02014.737695799.991992800.0399919.319995
2023-03-15 00:15:00.0000.00.0480.00.0NaN0.00.01.707277-1.9690510.049.5999910.02014.7376950.000000800.0399919.240035
2023-03-15 00:30:00.0000.00.0480.00.0NaN0.00.01.707277-1.9690510.049.5999910.02014.737695799.991992800.0399919.312000
2023-03-15 00:45:00.0000.00.0480.00.0NaN0.00.01.707277-1.9690510.049.5999910.02014.737695799.991992800.0399919.079940
2023-03-15 01:00:00.0000.00.0480.00.0NaN0.00.01.707277-1.9690510.049.5999910.02014.737695799.991992800.0399919.192004
2023-03-15 01:15:00.0000.00.0480.00.0NaN0.00.01.707277-1.9690510.049.5999910.02014.737695799.991992800.0399919.192997
2023-03-15 01:30:00.0000.00.0480.00.0NaN0.00.01.707277-1.9690510.049.5999910.02014.737695799.991992800.0399919.184437
2023-03-15 01:45:00.0000.00.0480.00.0NaN0.00.01.707277-1.9690510.049.5999910.02014.737695799.991992800.0399919.247998
2023-03-15 02:00:00.0000.00.0480.00.0NaN0.00.01.707277-1.9690510.049.5999910.02014.737695799.991992800.0399919.159998
2023-03-15 02:15:00.0000.00.0480.00.0NaN0.00.01.707277-1.9690510.049.5999910.02014.737695799.991992800.0399918.976328
14617_FERM0101.Agitation_PV14617_FERM0101.Air_Sparge_PV14617_FERM0101.Biocontainer_Pressure_PV14617_FERM0101.DO_1_PV14617_FERM0101.DO_2_PV14617_FERM0101.Gas_Overlay_PV14617_FERM0101.Load_Cell_Net_PV14617_FERM0101.pH_1_PV14617_FERM0101.pH_2_PV14617_FERM0101.PUMP_1_PV14617_FERM0101.PUMP_1_TOTAL14617_FERM0101.PUMP_2_PV14617_FERM0101.PUMP_2_TOTAL14617_FERM0101.Single_Use_DO_PV14617_FERM0101.Single_Use_pH_PV14617_FERM0101.Temperatura_PV
DateTime
2024-09-10 21:45:00.00020.00.0-1.0069460.0NaN4.0001611567.25.760351-1.4744040.017.3600010.00.0799.9919925.8959969.128003
2024-09-10 22:00:00.00020.00.0-1.0653060.0NaN3.9999991567.25.768329-1.4744040.017.3600010.00.0799.9919925.8959969.095996
2024-09-10 22:15:00.00020.00.0-0.9317980.0NaN4.0000851567.25.768329-1.4744040.017.3600010.00.0799.9919925.8959969.030962
2024-09-10 22:30:00.00020.00.0-1.0699610.0NaN3.9998801567.25.768329-1.4744040.017.3600010.00.0799.9919925.8959969.005624
2024-09-10 22:45:00.00020.00.0-1.0129100.0NaN3.9999651567.25.776022-1.4744040.017.3600010.00.0799.9919925.8959968.959998
2024-09-10 23:00:00.00020.00.0-1.0069460.0NaN4.0002451567.25.768329-1.4744040.017.3600010.00.0799.9919925.8959968.919995
2024-09-10 23:15:00.00020.00.0-1.0474550.0NaN3.9997241567.25.776022-1.4744040.017.3600010.00.0799.9919925.8959968.880005
2024-09-10 23:30:00.00020.00.0-1.0334300.0NaN4.0000131567.25.776022-1.4744040.017.3600010.00.0799.9919925.8959968.840002
2024-09-10 23:45:00.00020.00.0-1.0879640.0NaN4.0000771567.25.768329-1.4744040.017.3600010.00.0799.9919925.8959968.807996
2024-09-11 00:00:00.00020.00.0-0.9948730.0NaN4.0003411567.25.776022-1.4744040.017.3600010.00.0799.9919925.8959968.775166

Duplicate rows

Most frequently occurring

14617_FERM0101.Agitation_PV14617_FERM0101.Air_Sparge_PV14617_FERM0101.Biocontainer_Pressure_PV14617_FERM0101.DO_1_PV14617_FERM0101.DO_2_PV14617_FERM0101.Gas_Overlay_PV14617_FERM0101.Load_Cell_Net_PV14617_FERM0101.pH_1_PV14617_FERM0101.pH_2_PV14617_FERM0101.PUMP_1_PV14617_FERM0101.PUMP_1_TOTAL14617_FERM0101.PUMP_2_PV14617_FERM0101.PUMP_2_TOTAL14617_FERM0101.Single_Use_DO_PV14617_FERM0101.Single_Use_pH_PV14617_FERM0101.Temperatura_PV# duplicates
2067NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN4446
15960.00.0480.00.0NaN0.00.01.707277-1.9690510.049.5999910.02014.737695799.991992800.0399916.55200221
15850.00.0480.00.0NaN0.00.01.707277-1.9690510.049.5999910.02014.737695799.991992800.0399916.38399720
15830.00.0480.00.0NaN0.00.01.707277-1.9690510.049.5999910.02014.737695799.991992800.0399916.35200218
15910.00.0480.00.0NaN0.00.01.707277-1.9690510.049.5999910.02014.737695799.991992800.0399916.47199718
9670.00.0480.00.0NaN0.0-1.21.707277-1.9690510.049.5999910.02014.737695799.991992800.0399916.06400117
10090.00.0480.00.0NaN0.0-1.21.707277-1.9690510.049.5999910.02014.737695799.991992800.0399916.72800317
10150.00.0480.00.0NaN0.0-1.21.707277-1.9690510.049.5999910.02014.737695799.991992800.0399916.81600317
10170.00.0480.00.0NaN0.0-1.21.707277-1.9690510.049.5999910.02014.737695799.991992800.0399916.85600617
15730.00.0480.00.0NaN0.00.01.707277-1.9690510.049.5999910.02014.737695799.991992800.0399916.19200417